activity
20232026
most citedElasticAI: Creating and Deploying Energy-Efficient Deep Learning Accelerator for Pervasive Computing

10 citations · 23 across the 4 of their papers we have counts for

collaborators

8 papers

cs.AR20264 cited

Energy Efficient LSTM Accelerators for Embedded FPGAs through Parameterised Architecture Design

Chao Qian, Tianheng Ling, Gregor Schiele

Long Short-term Memory Networks (LSTMs) are a vital Deep Learning technique suitable for performing on-device time series analysis on local sensor data streams of embedded devices.…

eess.SP2025

StrikeWatch: Wrist-worn Gait Recognition with Compact Time-series Models on Low-power FPGAs

Tianheng Ling, Chao Qian, Peter Zdankin +2

Running offers substantial health benefits, but improper gait patterns can lead to injuries, particularly without expert feedback. While prior gait analysis systems based on camera…

cs.LG2025

Enabling Vibration-Based Gesture Recognition on Everyday Furniture via Energy-Efficient FPGA Implementation of 1D Convolutional Networks

Koki Shibata, Tianheng Ling, Chao Qian +4

The growing demand for smart home interfaces has increased interest in non-intrusive sensing methods like vibration-based gesture recognition. While prior studies demonstrated feas…

cs.LG2025

Automating Versatile Time-Series Analysis with Tiny Transformers on Embedded FPGAs

Tianheng Ling, Chao Qian, Lukas Johannes Haßler +1

Transformer-based models have shown strong performance across diverse time-series tasks, but their deployment on resource-constrained devices remains challenging due to high memory…

cs.LG2024

Resource-aware Mixed-precision Quantization for Enhancing Deployability of Transformers for Time-series Forecasting on Embedded FPGAs

Tianheng Ling, Chao Qian, Gregor Schiele

This study addresses the deployment challenges of integer-only quantized Transformers on resource-constrained embedded FPGAs (Xilinx Spartan-7 XC7S15). We enhanced the flexibility…

cs.AR202410 cited

ElasticAI: Creating and Deploying Energy-Efficient Deep Learning Accelerator for Pervasive Computing

Chao Qian, Tianheng Ling, Gregor Schiele

Deploying Deep Learning (DL) on embedded end devices is a scorching trend in pervasive computing. Since most Microcontrollers on embedded devices have limited computing power, it i…